Mateus de Freitas Virgílio Pereira

On Nonlinear Dynamics and Flight Control at High Angles of Attack With Uncertain Aerodynamics

In this paper we consider the flight dynamics of fighter aircraft at high angles of attack with uncertain aerodynamic coefficients. Stochastic parametric uncertainty is dealt with by employing spectral decomposition of the random variables by means of the generalized polynomial chaos expansion. We propose an optimal linear feedback strategy for the automatic pilot system to recover the aircraft from stall and provide acceptable dynamic response. Optimality of the proposed control law is proved by solving the Hamilton-Jacobi-Bellman equation and asymptotically stability of the controlled nonlinear aircraft model is guaranteed in the Lyapunov sense. Numerical results are verified with Monte-Carlo simulations.Copyright

Experimental evaluation of HJB optimal controllers for the attitude dynamics of a multirotor aerial vehicle

The present paper is concerned with the design and experimental evaluation of optimal control laws for the nonlinear attitude dynamics of a multirotor aerial vehicle. Three design methods based on Hamilton-Jacobi-Bellman equation are taken into account. The first one is a linear control with guarantee of stability for nonlinear systems. The second and third are a nonlinear suboptimal control techniques. These techniques are based on an optimal control design approach that takes into account the nonlinearities present in the vehicle dynamics. The stability Proof of the closed-loop system is presented. The performance of the control system designed is evaluated via simulations and also via an experimental scheme using the Quanser 3-DOF Hover. The experiments show the effectiveness of the linear control method over the nonlinear strategy.

Hovering Analysis of a Tethered Multirotor under External Disturbances

CON-2016-1210 Abstract: Multirotor Aerial Vehicles (MAVs) have been the subject of many academic studies and have attracted a lot of attention from industry in recent years. MAVs have flight capabilities such as hovering, Vertical Take-Off and Landing (VTOL) and agile maneuvering capability, which cannot be achieved by conventional fixed wing aircraft. However, such vehicles have limited autonomy, which results in flights of at most some minutes. A sub-category of aerial vehicles is tethered MAVs, which are anchored at a fixed point by a cable. While this limits their motion, it can also works as a power line, providing electrical power to the vehicle and enhancing its flight autonomy. This paper presents a modeling and hovering control strategy for tethered MAV. The vehicle is an octocopter, with flat configuration. A viscoelastic model is considered for the cable, in order to reproduce its dynamic behavior. The cable consists of a spring and a damper in parallel. The controller is based on a saturated state feedback control, thus simplifying the controller. The model was evaluated through numerical simulations using MATLAB/Simulink. The vehicle performed a hover flight and was subjected to external disturbances in order to emulate ambient wind. The results for the tethered MAV were compared with the MAV flying freely without cable. The tethered MAV presents improved hovering capability when compared with the vehicle without cable, due to the tension exerted by the cable, which provides a better robustness to exogenous perturbations.

Polynomial Chaos-based analysis of Multirotor Aerial Vehicles Model Subjected to Uncertainties

Abstract: Uncertainties are ubiquitous in mathematical equations that represent physical models and may come from unknown plant parameters or from the purposeful choice of a simplified representation of the system dynamics. In case of Multirotor Aerial Vehicles (MAVs), which have become interesting for applications where manned operations are considered inefficient and dangerous for humans, uncertainties such as inaccurate parameters, neglect of gyroscopic effect, blade flapping, as well as, wind gusts can have strong adverse effects on the system behavior. This paper uses the Polynomial Chaos Expansion method to propagate uncertainties in the multirotor model and characterize the effects over the vehicle trajectory, considering constraints on both the total thrust magnitude and the inclination of the rotor plane in the design of the position control. The Polynomial Chaos method constructs meta-models that accurately mimic the behavior of systems with uncertainty about the mean of stochastic inputs. The uncertainties are described in terms of normal distribution. The results are compared with Monte Carlo simulations.